GTI: A Scalable Graph-based Trajectory Imputation

GPS-enabled devices, including vehicles, smartphones, wearable and tracking devices, as well as various check-in and social network data are continuously producing tremendous amounts of trajectory data, which are used consistently in many applications such as urban planning and map inference. Existing techniques for trajectory data imputation rely heavily on the existing maps to perform map-matching operations. However, modern applications such as map construction and map update assume no map exists. In this paper, we propose GTI - a scalable graph-based trajectory imputation approach for trajectory data completion. GTI relies on cross-trajectory imputation, as it exploits "mutual information" of the aggregated knowledge of all input sparse trajectories to impute the missing data for each single one of them. GTI can act as a pre-processing step for any trajectory data management system or trajectory-based application, as it takes raw sparse trajectory data as its input and outputs dense imputed trajectory data that significantly increase the accuracy of different systems that consume trajectory data. We evaluate GTI on junction-scale as well as city-scale real datasets. In addition, GTI is used as a pre-processing step in multiple trajectory-based applications and it boosts the accuracy across these applications compared with the state-of-the-art work.